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Feature-Based Diversity Optimization for Problem Instance Classification.

Wanru Gao1, Samadhi Nallaperuma2, Frank Neumann3

  • 1School of Information Engineering, Zhengzhou University, Zhengzhou, Henan, China kelly_gwr@hotmail.com.

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Summary
This summary is machine-generated.

This study introduces a framework to create challenging or simple Traveling Salesperson Problem (TSP) instances for the 2-OPT heuristic. Evolutionary algorithms generate diverse problem sets, aiding in understanding heuristic search behavior.

Keywords:
Combinatorial optimizationTravelling Salesman Problemclassificationevolving instances.feature selection

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Area of Science:

  • Artificial Intelligence
  • Operations Research
  • Computer Science

Background:

  • Understanding heuristic search methods, like 2-OPT for the Traveling Salesperson Problem (TSP), is complex.
  • Developing methods to systematically generate problem instances that are hard or easy for specific heuristics is crucial for algorithm analysis.

Purpose of the Study:

  • To present a general framework for constructing diverse problem instances tailored to the difficulty they pose for a given search heuristic.
  • To generate instance sets that are diverse concerning various features of the underlying problem structure.

Main Methods:

  • Utilizing an evolutionary algorithm to construct a diverse set of Traveling Salesperson Problem (TSP) instances.
  • Employing instance features to classify problems based on their difficulty for the 2-OPT heuristic.

Main Results:

  • Demonstrated that a general framework can construct diverse TSP instances that are either hard or easy for the 2-OPT heuristic.
  • Identified combinations of two or three instance features that effectively classify TSP instances by their solvability using 2-OPT.

Conclusions:

  • The proposed framework successfully generates varied TSP instances, aiding in the analysis of heuristic search performance.
  • Specific combinations of problem features can accurately predict the difficulty of TSP instances for the 2-OPT algorithm, enhancing our understanding of heuristic behavior.